1. Field of the Invention
This invention relates generally to semiconductor device manufacturing and, more particularly, to a method and apparatus for distinguishing between sources of process variation.
2. Description of the Related Art
There is a contrast drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a lot of wafers using a variety of processing tools, including photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal processing tools, implantation tools, etc. The technologies underlying semiconductor processing tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the processing tools that are currently commercially available suffer certain deficiencies. In particular, such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender nonoptimal control of critical processing parameters, such as throughput, accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
Typically, during the fabrication of a semiconductor wafer, a metrology event immediately follows a process event to monitor the performance of the process. Multiple metrology tools may be employed to measure several metrics related to the process performance. After such metrology, adjustments to the operating parameters of process may be made to control the process output in light of a target value. Certain metrology tests, such as electrical performance tests (e.g., effective gate length and drive current), are not performed until many processing steps have been performed (i.e., typically after the first metal layer is formed). It is only at this point that the success of certain previous process events is evident. Although the electrical tests may identify a performance drift, the temporal displacement between the numerous process events and the metrology event makes it difficult to identify the source of the drift or readily adjust the process to account for the drift. Another condition that exacerbates the difficulty in identifying the source of a fault is the number of processing and metrology tools in the process flow. Typically, more than one tool for performing a particular process or metrology step is provided in the manufacturing facility. Each particular lot of wafers may pass through an entirely different set of tools during its production.
There are various ways for identifying process drifts. Generally, metrology data (i.e., performance or process) is gathered and evaluated against various rules to determine if an error condition has occurred. Although, various rules may be used, many companies have adopted the “Western Electric Rules”, originally developed by the Western Electric Company. The results specify that an error occurs if:                Rule 1: One measurement exceeds three standard deviations from the target (i.e., 1>3σ);        Rule 2: Two out of three consecutive measurements exceed two standard deviations from the target on one side of the target (i.e., 2/3>2σ);        Rule 3: Four out of Five consecutive measurements exceed one standard deviation from the target on one side of the target (i.e., 4/5>σ); and        Rule 4: Eight consecutive points on one side of the target.        
Rule 1 and 2 violations are typically associated with process faults or equipment failures. Rule 3 and 4 violations are most often useful for identifying process drifts. Process drifts may result in shifts in feature dimensions, such as oxide thickness or gate electrode length, for example. Such drifts may result in degraded performance of the final product or may cause difficulty for subsequent processing steps. Some drifts may also be the result of errant metrology tools. For example, if the calibration on a metrology tool used to measure process layer thickness is out of specification, the metrology information it provides to the manufacturing control system may be inaccurate. The control system may attempt to adjust the process to account for the drift in metrology data and actually compound the problem. Another potential cause for process drifts is changes to the operating parameters of a particular tool. Still another source for process variation is the characteristics of the incoming wafers to be processed in the tool prior to the metrology event that identifies the trend. For example, if a particular group of wafers has a dished or domes surface profile, the effectiveness of subsequent etch or polishing processes may be affected. A drift may not be caused by the etch or polishing tool, but rather due to the characteristics of the incoming wafers.
Because there are many sources of process variations, it is difficult to identify the actual source and take appropriate corrective actions. If the source of the process variation is misidentified, the corrective actions may serve to increase the process variation rather than compensate for it.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.